Computational Intelligence in Time Series Forecasting

Palit, Ajoy K.; Popovic, Dobrivoje

ISBN 10: 1852339489 / ISBN 13: 9781852339487

Editore: Springer London Ltd, 2005

Riguardo questo articolo

Deals with the power of intelligent technologies individually and in combination. This book includes examples of the particular systems and processes susceptible to each technique. It is suitable for industrial training purposes, as well as serving as a useful reference material for experimental researchers. Series: Advances in Industrial Control. Num Pages: 372 pages, 10 black & white tables, biography. BIC Classification: PBT; UYQ. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 22. Weight in Grams: 730. . 2005. Hardback. . . . . Books ship from the US and Ireland.

Codice articolo V9781852339487

Dati bibliografici

• Titolo: Computational Intelligence in Time Series Forecasting
• Editore: Springer London Ltd
• Data di pubblicazione: 2005
• Lingua: Inglese
• ISBN 10: 1852339489
• ISBN 13: 9781852339487
• Rilegatura: Rilegato
• Condizione: New

Descrizione articolo

Riassunto

Foresight in an engineering business can make the difference between success and failure, and can be vital to the effective control of industrial systems. The authors of this book harness the power of intelligent technologies individually and in combination.

Dalla quarta di copertina

Foresight in an engineering enterprise can make the difference between success and failure and can be vital to the effective control of industrial systems. Forecasting the future from accumulated historical data is a tried and tested method in areas such as engineering finance. Applying time series analysis in the on-line milieu of most industrial plants has been more problematic because of the time and computational effort required. The advent of soft computing tools such as the neural network and the genetic algorithm offers a solution.

Chapter by chapter, Computational Intelligence in Time Series Forecasting harnesses the power of intelligent technologies individually and in combination. Examples of the particular systems and processes susceptible to each technique are investigated, cultivating a comprehensive exposition of the improvements on offer in quality, model building and predictive control, and the selection of appropriate tools from the plethora available; these include:

• forecasting electrical load, chemical reactor behaviour and high-speed-network congestion using fuzzy logic;

• prediction of airline passenger patterns and of output data for nonlinear plant with combination neuro-fuzzy networks;

• evolutionary modelling and anticipation of stock performance by the use of genetic algorithms.

Application-oriented engineers in process control, manufacturing, the production industries and research centres will find much to interest them in Computational Intelligence in Time Series Forecasting and the book is suitable for industrial training purposes. It will also serve as valuable reference material for experimental researchers.

 

Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.